Studies On Engine Machining Equipment Condition Monitoring Strategy

This dissertation researches engine machining equipment through condition monitoring and fault diagnosis. By studying high-speed machining center of it, I clarify the FFT and wavelet combined technology is a good means to establish predictive maintenance system. Because engine machining equipments in the processing center is the key parts of an engine factory production line, the time period is to learn faults in the high frequency spindle–- the foremost part of the machining center. Then to prevent sudden machine down on the machining center, ensure safety in production, reduce maintenance cost, improve management of equipment.I introduced the idea of condition monitoring and fault diagnosis. After briefing its origin and development I elaborate Failure Modes & Effects Analysis—FEMA, which includes fault modes, fault cause, fault effect, severity of fault and difficulty of detecting fault.Typically, related knowledge is needed in this dissertation. Based on vibration signals I can get information about the condition of machining center, by using spectrum analysis technology. To do this, layouts of acceleration senor and features of high frequency spindle are also involved.Indeed, what I studies is rotating machinery which has its inherent fault mode. Data have been collected from spindle on the test stand which I construct for the research. I use these data to examine related theories. The computing results lead to my conclusion—using FFT and wavelet analysis combined technology can effectively diagnose fault and locate problem. By propelling changes in maintenance our company has cut lots of costs. Apparently, condition monitoring and fault diagnosis provide basis for predictive maintenance.